RNG Algorithm Comparator

One of the oldest and simplest pseudo-random number generators. Uses the formula: X(n+1) = (a * X(n) + c) mod m

Pros: Fast, simple, memory efficient

Cons: Poor quality, short period, correlation in lower bits

Use case: Simple simulations where quality is not critical

A widely-used PRNG with excellent statistical properties and a very long period (2^19937 - 1).

Pros: Excellent statistical properties, long period, passes most statistical tests

Cons: Not cryptographically secure, uses more memory, slower initialization

Use case: Monte Carlo simulations, scientific computing, games

A fast PRNG using XOR and bit-shift operations. Very efficient with reasonable quality.

Pros: Very fast, small state, good quality for most purposes

Cons: Not cryptographically secure, shorter period than MT

Use case: Games, simulations where speed is important

A simple and fast 32-bit PRNG with good statistical properties.

Pros: Fast, simple, good quality for a 32-bit generator

Cons: 32-bit period, not cryptographically secure

Use case: Quick simulations, testing, procedural generation

• Mean: Should be close to 0.5 for uniform [0,1] distribution
• Variance: Should be close to 1/12 ≈ 0.0833 for uniform [0,1]
• Chi-Square: Tests uniformity across bins (lower is better, p > 0.05 is good)
• Kolmogorov-Smirnov: Tests if distribution matches uniform (p > 0.05 is good)
• Runs Test: Tests for independence (checks for patterns)

• Distribution Histogram: Should be flat/uniform for good RNG
• 2D Pattern: Pairs of consecutive numbers; should show no patterns
• Serial Correlation: Should show no correlation between consecutive values